Reviewers that review data sets, for example, during electronic discovery (e-discovery), may encounter data sets that contain thousands of documents. The reviewers may not need to review all of the documents and may conduct a concept search of a data set to identify which documents are relevant for review. A concept search (or conceptual search) is an information retrieval method that is used to search electronically stored unstructured text (e.g., digital archives, email, etc.) for information that is conceptually similar to the information provided in a search query. In contrast, a key word search retrieves documents that contain the terms provided in a search query. In a concept search, the ideas expressed in the information retrieved are relevant to the ideas contained in the text of the concept search query. For example, a reviewer may wish to identify documents based on a search term “diamond.” The data set may include documents that describe baseball fields, but do not contain the word diamond itself. A key word search would not likely return any of these documents. However, a concept search would include the documents that describe baseball fields as part of the concept search results. The concept search results, however, may be over-inclusive and include documents that are not relevant to a user's interests. For example, a user may be interested in diamond in the context of baseball, but the concept search may also return documents that pertain to diamond jewelry, diamond shapes, etc. Traditional concept search tools do not offer a way to refine the concept search criteria to return results that are more relevant.